Method and related device for updating dishonest list of scenic spot tourists based on blockchain

Abstract

One or more embodiments of this specification disclose a method and related device for updating the dishonest list of scenic spot tourists based on blockchain. In this scheme, the dishonest list of tourists is shared between scenic spot service nodes as blockchain nodes based on the blockchain network. At the same time, the dishonest list of tourists can be updated using the recognition and evaluation model trained locally at the scenic spot service node using a machine learning algorithm. Therefore, the sharing of the dishonest list of tourists through blockchain provides a practical method for connecting the isolated islands of tourist data between multiple scenic spots and implementing relevant restrictions on tourists. At the same time, the dishonest list sharing method based on blockchain technology can also provide good data support for analyzing the behavioral data information of tourists on the dishonest list and setting corresponding normal recovery strategies. In addition, the machine learning algorithm is used to identify and evaluate tourists to update the dishonest list of tourists, ensuring that the evaluation results are more accurate and objective.

Description

Technical Field

[0001] This document relates to the field of blockchain technology, and in particular to a method and related device for updating a list of dishonest tourists in scenic spots based on blockchain. Background Art

[0002] Every scenic spot may receive tourists with bad tourist behavior, for example, illegally destroying scenic spot buildings, maliciously cutting in line when playing, occupying seats and smoking when taking public transportation to and from the scenic spot, etc. At present, the corresponding scenic spots generally adopt manual registration to include such tourists in the dishonest list to warn or avoid dishonest behavior.

[0003] However, the existing list of dishonest travelers is generally only used in local scenic spots and cannot be shared with other scenic spots. After sharing, the update of the list of dishonest travelers still relies on manual registration. Summary of the invention

[0004] The purpose of one or more embodiments of this specification is to provide a method and related device for updating the dishonest list of tourists in scenic spots based on blockchain, so as to realize the sharing of dishonest list data of tourists among multiple scenic spots, and at the same time, realize the updating and storage of dishonest list of tourists based on the identification and evaluation model.

[0005] To solve the above technical problems, one or more embodiments of this specification are implemented as follows:

[0006] In the first aspect, a method for updating a dishonest list of scenic spot tourists based on blockchain is proposed, which is applied to a blockchain network including multiple scenic spot service nodes. The method includes:

[0007] Each scenic spot service node in the blockchain network periodically collects historical behavior data of historical travelers visiting local scenic spots;

[0008] According to the historical behavior data and the behavior labels of historical passengers, a recognition evaluation model is obtained by training using a machine learning algorithm, and a list of dishonest passengers consisting of the identity information of passengers is created and stored on the chain. Multiple dishonest passenger lists are maintained on the blockchain, each dishonest passenger list corresponds to a scenic spot, and the identity information of the same passenger is allowed to appear in different dishonest passenger lists;

[0009] When any scenic spot service node receives an application from a tourist to visit a local scenic spot, the tourist’s identity information is sent to the blockchain for verification;

[0010] If the identity information of the passenger appears in at least one or more passenger dishonest lists, restricting the passenger from visiting;

[0011] If the identity information of the passenger does not appear in any of the passenger dishonest lists, the passenger will be allowed to tour freely;

[0012] After the tourist starts his tour, the scenic spot service node determines the tourist's evaluation result during this visit to the scenic spot based on the local recognition and evaluation model; based on the evaluation result, it decides whether to update the local tourist dishonest list and store it on the chain.

[0013] In the second aspect, a system for updating the dishonest list of scenic spot tourists based on blockchain is proposed, the system comprising: multiple scenic spot service nodes and blockchain;

[0014] Each scenic spot service node in the blockchain network periodically collects historical behavior data of historical travelers visiting local scenic spots;

[0015] According to the historical behavior data and the behavior labels of historical passengers, a recognition evaluation model is obtained by training using a machine learning algorithm, and a list of dishonest passengers consisting of the identity information of passengers is created and stored on the chain. Multiple dishonest passenger lists are maintained on the blockchain, each dishonest passenger list corresponds to a scenic spot, and the identity information of the same passenger is allowed to appear in different dishonest passenger lists;

[0016] When any scenic spot service node receives an application from a tourist to visit a local scenic spot, the tourist’s identity information is sent to the blockchain for verification;

[0017] If the identity information of the passenger appears in one or more passenger dishonest lists, the passenger's tour will be restricted;

[0018] If the identity information of the passenger does not appear in any of the passenger dishonest lists, the passenger will be allowed to tour freely;

[0019] After the tourist starts his tour, the scenic spot service node determines the tourist's evaluation result during this tour of the scenic spot based on the local recognition evaluation model; and decides whether to update the local tourist dishonest list and store it on the chain based on the evaluation result.

[0020] In a third aspect, an electronic device is provided, comprising:

[0021] Processor; and

[0022] A memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform operations performed by a scenic spot service node or a blockchain.

[0023] In a fourth aspect, a computer-readable storage medium is proposed, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device performs the operations performed by a scenic spot service node or a blockchain.

[0024] It can be seen from the technical solutions provided by one or more embodiments of the above specification that, based on the blockchain network, the sharing of the dishonest list of passengers between scenic area service nodes as blockchain nodes is realized. At the same time, the dishonest list of passengers can be updated using the identification and evaluation model obtained by training the local scenic area service node using the machine learning algorithm. Therefore, the sharing of the dishonest list of passengers through blockchain provides a practical method for connecting passenger data islands between multiple scenic spots and implementing relevant restrictions on passengers. At the same time, the dishonest list sharing method based on blockchain technology can also provide good data support for analyzing the behavioral data information of passengers on the dishonest list and setting corresponding normal recovery strategies. In addition, the machine learning algorithm is used to identify and evaluate passengers to update the dishonest list of passengers and ensure that the evaluation results are more accurate and objective. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the description of one or more embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0026] Figure 1 This is a schematic diagram of the scenario architecture of a solution for updating the dishonest list of scenic spot tourists based on blockchain provided in an embodiment of this specification.

[0027] Figure 2 This is one of the step diagrams of a method for updating a dishonest list of scenic spot tourists based on blockchain provided in an embodiment of this specification.

[0028] Figure 3 This is the second step diagram of a method for updating a dishonest list of scenic spot tourists based on blockchain provided in an embodiment of this specification.

[0029] Figure 4 It is a structural diagram of a system for updating a dishonest list of scenic spot tourists based on blockchain provided in an embodiment of this specification.

[0030] Figure 5 It is a schematic diagram of the structure of an electronic device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the one or more embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0032] Blockchain is a decentralized and innovative solution that uses distributed ledger technology to solve multi-party trust issues. It is a cutting-edge technology in today's society.

[0033] This application is based on the blockchain network to realize the sharing of the dishonest list of passengers between scenic spot service nodes as blockchain nodes. At the same time, the dishonest list of passengers can be updated using the identification and evaluation model trained locally at the scenic spot service node using a machine learning algorithm. Therefore, the sharing of the dishonest list of passengers through blockchain provides a practical method for connecting passenger data islands between multiple scenic spots and implementing relevant restrictions on passengers. At the same time, the dishonest list sharing method based on blockchain technology can also provide good data support for analyzing the behavioral data information of passengers on the dishonest list and setting corresponding normal recovery strategies. In addition, the machine learning algorithm is used to identify and evaluate passengers to update the dishonest list of passengers and ensure that the evaluation results are more accurate and objective.

[0034] Reference Figure 1 As shown, it is a schematic diagram of the scenario architecture of the scheme for updating the dishonest list of scenic spot tourists based on blockchain provided in the embodiment of this specification. The scenario architecture includes: multiple scenic spot service nodes 102 and blockchain 104. Among them, the scenic spot service node 102 here can refer to the scenic spot backend server, which collects the tourist behavior data through external collection devices (cameras, ticket machines, etc.). In fact, it can also be assisted by manual entry.

[0035] Embodiment 1

[0036] Reference Figure 2 As shown, a schematic diagram of a method for updating a dishonest list of scenic spot tourists based on blockchain provided in an embodiment of this specification is applied to a blockchain network including multiple scenic spot service nodes. The method may include the following steps:

[0037] Step 202: Each scenic spot service node in the blockchain network periodically collects historical behavior data of historical tourists visiting the local scenic spot.

[0038] In the embodiments of the present specification, the passenger's behavior data may include: consumption information, trajectory information, length of stay, and violation screen information.

[0039] It should be understood that when collecting passenger behavior data, it can be collected through external devices such as cameras, scanners, vending terminals, etc. These behavior data can be uniformly stored in the corresponding database and purposefully obtained when needed.

[0040] Step 204: Based on the historical behavior data and the behavior labels of historical travelers, a recognition and evaluation model is trained using a machine learning algorithm, and a dishonest traveler list consisting of the identity information of the travelers is created and stored on the chain. Multiple dishonest traveler lists are maintained on the blockchain, each dishonest traveler list corresponds to a scenic spot, and the identity information of the same traveler is allowed to appear in different dishonest traveler lists.

[0041] In the embodiments of this specification, the machine learning algorithm involved may include: a logistic regression algorithm or a support vector machine algorithm. In addition, other machine learning algorithms may also be used, such as: a neural network algorithm, etc.

[0042] After obtaining the historical behavior data, feature extraction is performed on the historical behavior data of each historical passenger. Then, based on the behavior labels of the historical passengers, a recognition and evaluation model is trained using a logistic regression algorithm, a support vector machine algorithm, or a random forest algorithm.

[0043] It should be understood that the behavior label of historical travelers can be "white" indicating good travel behavior or "black" indicating bad travel behavior. In this way, while training the recognition and evaluation model, historical travelers with behavior labels of "black" can be counted together according to the behavior labels of historical travelers to create a list of travelers with bad faith.

[0044] After creating a dishonest list of tourists, the scenic spot service node can also send the dishonest list of tourists to the blockchain for storage. In this way, the blockchain will store dishonest lists of tourists sent by multiple scenic spot service nodes. Generally, each dishonest list of tourists corresponds to a scenic spot (i.e., scenic spot service node).

[0045] Step 206: When any scenic spot service node receives an application from a tourist to visit a local scenic spot, the tourist's identity information is sent to the blockchain for verification.

[0046] Step 208: If the identity information of the passenger appears in one or more passenger dishonest lists, the passenger is restricted from touring.

[0047] Optionally, if the identity information of the passenger appears in one or more passenger dishonest lists, a matching restricted tour strategy is determined for the passenger based on the number of passenger dishonest lists where the passenger's identity information appears, and the restricted tour strategy performs partial or full restrictions on at least one or more dimensions of tour time, tour scope, and tour consumption.

[0048] For example, if a tourist's identity information only appears in the local scenic spot's dishonest tourist list, the tourist can be restricted from visiting part of the local scenic spot during the specified sightseeing time based on the tourist's credit status and work unit; or, the tourist can be prohibited from visiting the local scenic spot without restricting visiting other scenic spots.

[0049] For example, if a tourist's identity information appears not only in the tourist dishonesty list corresponding to the local scenic spot, but also in the tourist dishonesty list corresponding to other scenic spots, the tourist can be prohibited from visiting all alliance scenic spots based on the number of appearances; or, based on the number of appearances, as well as the tourist's credit status, work unit, etc. as endorsements, the tourist can be restricted from visiting part of the local scenic spot within the specified sightseeing time.

[0050] To give another example, if a tourist's identity information does not appear in the tourist dishonesty list corresponding to the local scenic spot, but appears in the tourist dishonesty list corresponding to other scenic spots, the tourist is allowed to freely visit the local scenic spot; or, based on the number of appearances, the tourist's credit status, work unit, etc., the tourist is restricted from visiting part of the local scenic spot within the specified sightseeing time.

[0051] Step 210: If the identity information of the passenger does not appear in any passenger dishonest list, the passenger is allowed to tour freely.

[0052] Sharing the dishonest list of travelers through blockchain provides a practical method for breaking through the isolated islands of traveler data between multiple scenic spots and implementing relevant travel restrictions on travelers. At the same time, the dishonest list sharing method based on blockchain technology can also provide good data support for analyzing the behavioral data information of travelers on the dishonest list and setting corresponding recovery strategies. In addition, machine learning algorithms are used to identify and evaluate travelers to update the dishonest list of travelers and ensure that the evaluation results are more accurate and objective.

[0053] Further, refer to Figure 3 As shown, after the tourist starts to tour, the method may further include:

[0054] Step 212: The scenic spot service node determines the evaluation result of the tourist during this scenic spot visit based on the local recognition evaluation model.

[0055] Method 1: Logistic regression algorithm

[0056] The logistic regression algorithm trains the model and optimizes the model parameters on a training data set consisting of pre-prepared passenger labels (i.e., behavioral labels that characterize whether the passenger is on the dishonest list, if yes, the behavioral label is "black", otherwise, the label is "white") and passenger behavior data. The trained logistic regression algorithm model will give appropriate weight values ​​to various behavioral data characteristics of passengers in the scenic area (in the algorithm model, the various behavioral data reflecting the passenger in the scenic area are referred to as the characteristics or characteristic values ​​of the passenger); then, the weight values ​​in the above-mentioned trained algorithm model can be used to calculate the weighted value of the behavioral data of a specific new passenger and then import it into the relevant decision function to obtain a prediction result of whether the passenger should be on the dishonest list.

[0057] Accordingly, the scenic spot service node determines the evaluation result of the tourist during this scenic spot visit based on the local recognition evaluation model, specifically including:

[0058] Get the behavior data of the tourist during this visit to the scenic spot and input it into the local recognition and evaluation model:

[0059]

[0060] where f θ (x) is the decision function, x is the feature vector composed of the passenger's behavior data, θ is the weight vector corresponding to the feature vector in the trained logistic regression model; T represents transposition;

[0061] Output evaluation results:

[0062]

[0063] Method 2: Support Vector Machine Algorithm

[0064] The support vector machine algorithm separates passengers labeled as "black" and "white" by constructing an optimal separation hyperplane in the feature space composed of the characteristics of the passenger's behavior data. Similar to the logistic regression algorithm, the support vector machine algorithm model that can distinguish passenger behavior labels also requires a pre-prepared training data set to train the model. By constructing an algorithm model with an optimal separation hyperplane on the training data set, the passenger's behavior label can be given according to the passenger's behavior feature data. Among them, the support vector machine algorithm model will solve the constrained optimization problem composed of the following formulas (4) and (5) based on the data of the training set to construct the optimal separation hyperplane.

[0065] Accordingly, the scenic spot service node determines the evaluation result of the tourist during this scenic spot visit based on the local recognition evaluation model, specifically including:

[0066] Get the behavior data of the tourist during this visit to the scenic spot, input it into the local recognition and evaluation model and output the result:

[0067]

[0068] Among them, ω and b are the normal vector and intercept that determine the hyperplane, x i are the characteristic vectors of the passenger's behavior data, T represents the transpose; and ω and b are determined by the following two formulas:

[0069]

[0070] sty i (ω T x i +b)≥1 (5)

[0071] in, Indicates finding the minimum value of an expression with ω and b as parameters, sty i (ω T x i +b)≥1 means to seek The minimum value of is the condition that the model parameters and eigenvectors need to satisfy.

[0072] In addition, the random forest algorithm can also be used to determine the evaluation results of the traveler during this scenic spot visit.

[0073] Random forest is a homogeneous ensemble algorithm based on the decision tree model. That is, random forest obtains multiple different training data subsets from the same training data set, builds a decision tree model on each data subset, and all decision tree models constitute a random forest model. For a random forest model with n decision trees, for an input test data, the discrete prediction category or continuous prediction result of the data is obtained by majority voting or weighted average of the prediction category or prediction result of n decision trees.

[0074] When the random forest model constructs a decision tree based on the training data set, it selects features and cutoff values ​​from the training data subset based on information entropy or Gini impurity, and constructs branches and leaf nodes of the decision tree based on the above information. Each trained decision tree is a weak classifier with high prediction accuracy for some samples; after combining a large number of such weak classifiers, a random forest model with low overfitting, high prediction accuracy and good generalization can be obtained.

[0075] In the new integrated model, the behavior data of each tourist will be input as a feature into each sub-model of the integrated model; each sub-model outputs the predicted behavior label (black or white) of the corresponding tourist based on the behavior data features; finally, the prediction result of the behavior label of a specific tourist by the integrated model is obtained by the prediction result of the sub-model through majority voting. By combining three heterogeneous algorithms as an integrated model, the advantages of different algorithms in structural mining and modeling at different levels of training data can be fully utilized, and the behavior label results of tourists can be predicted more accurately based on their tour behavior data. In addition to the three algorithms listed above, other classification algorithms including Bayesian algorithm, multi-layer perceptron, and deep learning classification algorithm are all within the scope of protection of this patent.

[0076] Step 214: Based on the evaluation results, decide whether to update the local travelers’ dishonest list and store it on the chain.

[0077] Optionally, step 214 may specifically include:

[0078] Based on the evaluation results, decide whether to add the passenger's identity information to the local passenger dishonesty list and store it on the blockchain; or

[0079] Based on the evaluation results and the preset restoration strategy, it is determined whether the passenger meets the restoration conditions. If so, the passenger's identity information is deleted from the local passenger dishonest list and updated on the chain; if not, no action is taken.

[0080] In the embodiments of this specification, the dishonest list of passengers can also be divided into levels according to the severity of the passenger's behavior; different levels correspond to different restriction strategies: for example, passengers with a high level of bad behavior are prohibited from visiting the alliance scenic spots within one year; for another example, the recovery strategy for passengers with a high level of bad behavior is related to personal credit. Passengers can also return to normal through relevant recovery strategies. The recovery plan can also involve an algorithm, and the dishonest list manager periodically collects information on the recovery of tourists. When the recovery conditions are met, the tourist's tour qualification can be restored.

[0081] In fact, information such as passenger trajectory information and passenger stay duration can also be used to plan the best tour route and recommend the best tourist attractions through relevant algorithms. It can also be shared and recommended to other scenic spots for tourists to choose.

[0082] Embodiment 2

[0083] Reference Figure 4 As shown, a system for updating a dishonest list of scenic spot tourists based on blockchain provided in an embodiment of this specification is provided, wherein the system 400 includes: a plurality of scenic spot service nodes 402 and a blockchain 404;

[0084] Each scenic spot service node 402 in the blockchain network periodically collects historical behavior data of historical travelers visiting local scenic spots;

[0085] According to the historical behavior data and the behavior labels of historical passengers, a recognition evaluation model is obtained by training using a machine learning algorithm, and a list of dishonest passengers consisting of the identity information of passengers is created and stored on the chain. Multiple dishonest passenger lists are maintained on the blockchain, each dishonest passenger list corresponds to a scenic spot, and the identity information of the same passenger is allowed to appear in different dishonest passenger lists;

[0086] When any scenic spot service node receives an application from a traveler to visit a local scenic spot, the identity information of the traveler is sent to the blockchain 404 for verification;

[0087] If the identity information of the passenger appears in one or more passenger dishonest lists, the passenger's tour will be restricted;

[0088] If the identity information of the passenger does not appear in any of the passenger dishonest lists, the passenger will be allowed to tour freely;

[0089] After the tourist starts to visit, the scenic spot service node determines the tourist's evaluation result during the current visit to the scenic spot based on the local recognition evaluation model;

[0090] Based on the evaluation results, it is decided whether to update the local travelers’ dishonest list and store the evidence on the chain.

[0091] In a specific implementation of the embodiment of this specification, the scenic area service node is also used to:

[0092] Get the behavior data of the tourist during this visit to the scenic spot and input it into the local recognition and evaluation model:

[0093]

[0094] where f θ (x) is the decision function, x is the feature vector composed of the passenger's behavior data, θ is the weight vector corresponding to the feature vector in the trained logistic regression model; T represents transposition;

[0095] Output evaluation results:

[0096]

[0097] In another specific implementation of the embodiment of this specification, the scenic area service node is further used to:

[0098] Get the behavior data of the tourist during this visit to the scenic spot, input it into the local recognition and evaluation model and output the result:

[0099]

[0100] Among them, ω and b are the normal vector and intercept that determine the hyperplane, x i are the characteristic vectors of the passenger's behavior data, T represents the transpose; and ω and b are determined by the following two formulas:

[0101]

[0102] sty i (ω T x i +b)≥1 (5)

[0103] in, Indicates finding the minimum value of an expression with ω and b as parameters, sty i (ω T x i +b)≥1 means to seek The minimum value of is the condition that the model parameters and eigenvectors need to satisfy.

[0104] In another specific implementation of the embodiment of this specification, the scenic area service node is further used to:

[0105] Based on the evaluation results, decide whether to add the identity information of the passenger to the local passenger dishonest list and store it on the blockchain;

[0106] or,

[0107] Based on the evaluation results and the preset restoration strategy, it is determined whether the passenger meets the restoration conditions. If so, the passenger's identity information is deleted from the local passenger dishonest list and updated on the chain; if not, no action is taken.

[0108] In another specific implementation of the embodiment of this specification, the scenic area service node is further used to:

[0109] If the identity information of the passenger appears in at least one passenger dishonest list, a matching restricted tour strategy is determined for the passenger based on the number of passenger dishonest lists where the passenger's identity information appears. The restricted tour strategy performs partial or full restrictions on at least one or more dimensions of tour time, tour scope, and tour consumption.

[0110] Sharing the dishonest list of travelers through blockchain provides a practical method for breaking through the isolated islands of traveler data between multiple scenic spots and implementing relevant travel restrictions on travelers. At the same time, the dishonest list sharing method based on blockchain technology can also provide good data support for analyzing the behavioral data information of travelers on the dishonest list and setting corresponding recovery strategies. In addition, machine learning algorithms are used to identify and evaluate travelers to update the dishonest list of travelers and ensure that the evaluation results are more accurate and objective.

[0111] Embodiment 3

[0112] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this specification. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0113] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0114] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0115] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a corresponding execution subject device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the operations in the first embodiment.

[0116] The above is as in this manual Figure 2The method performed by the device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in one or more embodiments of this specification can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in one or more embodiments of this specification can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0117] The electronic device may also perform Figure 2 The method and the corresponding device are implemented in Figure 2 The functions of the illustrated embodiments will not be described in detail in the embodiments of this specification.

[0118] Of course, in addition to software implementation, the electronic device of the embodiments of this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0119] Sharing the dishonest list of travelers through blockchain provides a practical method for breaking through the isolated islands of traveler data between multiple scenic spots and implementing relevant travel restrictions on travelers. At the same time, the dishonest list sharing method based on blockchain technology can also provide good data support for analyzing the behavioral data information of travelers on the dishonest list and setting corresponding recovery strategies. In addition, machine learning algorithms are used to identify and evaluate travelers to update the dishonest list of travelers and ensure that the evaluation results are more accurate and objective.

[0120] Embodiment 4

[0121] The embodiment of the present specification also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to execute Figure 2 The method of the embodiment shown is specifically used to perform the operations in embodiment one.

[0122] Sharing the dishonest list of travelers through blockchain provides a practical method for breaking through the isolated islands of traveler data between multiple scenic spots and implementing relevant travel restrictions on travelers. At the same time, the dishonest list sharing method based on blockchain technology can also provide good data support for analyzing the behavioral data information of travelers on the dishonest list and setting corresponding recovery strategies. In addition, machine learning algorithms are used to identify and evaluate travelers to update the dishonest list of travelers and ensure that the evaluation results are more accurate and objective.

[0123] In short, the above description is only a preferred embodiment of this specification and is not intended to limit the protection scope of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included in the protection scope of this specification.

[0124] The systems, devices, modules or units described in one or more of the above embodiments may be implemented by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0125] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0126] It should also be noted that the terms "include", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, commodity or device comprising the element.

[0127] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0128] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for updating a dishonest list of scenic spot tourists based on blockchain, applied to a blockchain network including multiple scenic spot service nodes, the method comprising: Each scenic spot service node in the blockchain network periodically collects historical behavior data of historical travelers visiting local scenic spots; According to the historical behavior data and the behavior labels of historical passengers, a recognition evaluation model is obtained by training using a machine learning algorithm, and a list of dishonest passengers consisting of the identity information of passengers is created and stored on the chain. Multiple dishonest passenger lists are maintained on the blockchain, each dishonest passenger list corresponds to a scenic spot, and the identity information of the same passenger is allowed to appear in different dishonest passenger lists; When any scenic spot service node receives an application from a tourist to visit a local scenic spot, the tourist’s identity information is sent to the blockchain for verification; If the identity information of the passenger appears in one or more passenger dishonest lists, the passenger's tour will be restricted; If the identity information of the passenger does not appear in any of the passenger dishonest lists, the passenger will be allowed to tour freely; After the tourist starts to visit, the scenic spot service node determines the tourist's evaluation result during the current visit to the scenic spot based on the local recognition and evaluation model, specifically including: obtaining the tourist's behavior data during the current visit to the scenic spot and inputting it into the local recognition and evaluation model: where f θ (x) is the decision function, x is the feature vector composed of the passenger's behavior data, θ is the weight vector corresponding to the feature vector in the trained logistic regression model; T represents transposition; Output evaluation results: Based on the evaluation results, it is decided whether to update the local travelers’ dishonest list and store the evidence on the chain.

2. The method for updating the dishonest list of scenic spot tourists based on blockchain as claimed in claim 1, determining whether to update the dishonest list of local tourists and store evidence on the chain based on the evaluation results, specifically includes: Based on the evaluation results, decide whether to add the identity information of the passenger to the local passenger dishonest list and store it on the blockchain; or, Based on the evaluation results and the preset whitewashing strategy, it is determined whether the passenger meets the whitewashing conditions. If so, the passenger's identity information is deleted from the local passenger dishonest list and updated on the chain; if not, no action is taken.

3. The method for updating the dishonest list of scenic spot tourists based on blockchain as claimed in claim 1 or 2, if the identity information of the tourist appears in at least one dishonest list of tourists, the tourist is restricted from visiting, specifically comprising: If the identity information of the passenger appears in at least one passenger dishonest list, a matching restricted tour strategy is determined for the passenger based on the number of passenger dishonest lists where the passenger's identity information appears. The restricted tour strategy performs partial or full restrictions on at least one or more dimensions of tour time, tour scope, and tour consumption.

4. A method for updating a dishonest list of scenic spot tourists based on blockchain, applied to a blockchain network including multiple scenic spot service nodes, the method comprising: Each scenic spot service node in the blockchain network periodically collects historical behavior data of historical travelers visiting local scenic spots; According to the historical behavior data and the behavior labels of historical passengers, a recognition evaluation model is obtained by training using a machine learning algorithm, and a list of dishonest passengers consisting of the identity information of passengers is created and stored on the chain. Multiple dishonest passenger lists are maintained on the blockchain, each dishonest passenger list corresponds to a scenic spot, and the identity information of the same passenger is allowed to appear in different dishonest passenger lists; When any scenic spot service node receives an application from a tourist to visit a local scenic spot, the tourist’s identity information is sent to the blockchain for verification; If the identity information of the passenger appears in one or more passenger dishonest lists, the passenger's tour will be restricted; If the identity information of the passenger does not appear in any of the passenger dishonest lists, the passenger will be allowed to tour freely; After the tourist starts to visit, the scenic spot service node determines the tourist's evaluation result during the current visit to the scenic spot based on the local recognition and evaluation model, specifically including: obtaining the tourist's behavior data during the current visit to the scenic spot, inputting the local recognition and evaluation model and outputting the result: Among them, ω and b are the normal vector and intercept that determine the hyperplane, x i is the characteristic vector of the passenger's behavior data, T represents the transpose; and ω and b are determined by the following two formulas: sty i (ω T x i +b)≥1 (5) where It means to find the minimum value of the expression with ω and b as parameters, sty i (ω T x i +b)≥1 means to seek The minimum value of is the condition that the model parameters and eigenvectors need to meet; Based on the evaluation results, it is decided whether to update the local travelers’ dishonest list and store the evidence on the chain.

5. The method for updating the dishonest list of scenic spot tourists based on blockchain as claimed in claim 4, determining whether to update the dishonest list of local tourists and store evidence on the chain based on the evaluation results, specifically includes: Based on the evaluation results, decide whether to add the identity information of the passenger to the local passenger dishonest list and store it on the blockchain; or, Based on the evaluation results and the preset whitewashing strategy, it is determined whether the passenger meets the whitewashing conditions. If so, the passenger's identity information is deleted from the local passenger dishonest list and updated on the chain; if not, no action is taken.

6. The method for updating the dishonest list of scenic spot tourists based on blockchain as claimed in claim 4 or 5, if the identity information of the tourist appears in at least one dishonest list of tourists, the tourist is restricted from visiting, specifically comprising: If the identity information of the passenger appears in at least one passenger dishonest list, a matching restricted tour strategy is determined for the passenger based on the number of passenger dishonest lists where the passenger's identity information appears. The restricted tour strategy performs partial or full restrictions on at least one or more dimensions of tour time, tour scope, and tour consumption.

7. A system for updating a dishonest list of scenic spot tourists based on blockchain, the system comprising: Multiple scenic area service nodes and blockchains; wherein the multiple scenic area service nodes or blockchains are used to execute the method described in any one of claims 1-3, or to execute the method described in any one of claims 4-6.

8. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the operations performed by the scenic area service node or blockchain in any one of claims 1 to 6.

9. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including multiple application programs, enables the electronic device to perform the operations performed by the scenic area service node or blockchain in any one of claims 1-6.

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